| This thesis designs efficient control signaling for resource allocation in OFDMA networks, with special attention given to improving the resource controller in the LTE standard.;We are interested in two aspects of resource controller design, the amount of control information a resource controller utilizes, and the performance, for instance the data spectral efficiency, it attains.;Our overall aim is to understand the fundamental tradeoff between these two quantities, and to learn how to design resource controllers that approach this tradeoff.;To get a sense of the state of the art in resource controller design, we first investigate the resource controller in the LTE standard, evaluating the amount of control information it requires.;We thoroughly catalog the physical layer signals related to resource allocation and link adaptation with a focus on the location and formatting of the pertinent reference and control signals, as well as the decisions they enable.;This enables us to determine the fraction of the time frequency resource grid spent on control information.;This control signaling overhead in LTE occupies a large percentage of the time-frequency footprint of wireless network traffic and is not efficiently encoded.;After this, we set about determining the fundamental tradeoff between the amount of control information and the spectral efficiency by modeling the problem using information theory.;In order to design more efficient control signaling schemes, we will model the control signals as messages in a distributed lossy source code, for which the rate distortion function describes an optimum tradeoff between a rate, reflecting the overhead, and a distortion, reflecting the performance.;Although there is no closed form expression for the rate region, we derive a novel adaptation of the Blahut-Arimoto algorithm to the CEO model with independent sources, and use it to numerically calculate the rate distortion function.;The developed algorithm is then utilized to calculate the rate distortion function for a series of simple resource allocation models.;Finally, for these physical layer resource allocation models, we design practical distributed quantizers that yield control signaling encodings for a resource controller that approaches the fundamental overhead performance tradeoff limit calculated. |
